get_forecast

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld annotations, the description discloses useful behavior: the forecast is split into three periods, the response includes specific weather fields, and ambiguous city/county names return both locations. This is meaningful operational context not available from annotations or schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded, the return summary is compact, and the Args line adds direct parameter guidance without padding. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with an output schema and safe-read annotations, the description covers scope, input flexibility, return shape, and edge-case behavior. Nothing essential for selecting or invoking the tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully carry parameter meaning. It does: city is defined as one of Taiwan's 22 cities/counties, accepts Chinese and English common spellings, includes concrete examples, and notes the 新竹/嘉義 dual-match case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific verb (查詢) and resource (台灣某縣市未來36小時天氣預報), and lists the returned fields. This makes the tool's purpose immediately distinguishable from the warning and earthquake siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clear intended context is given: querying a Taiwan city forecast for the next 36 hours, with input-writing conventions and the dual-match behavior for 新竹/嘉義. It does not explicitly say 'use get_weather_warnings for warnings' or similar exclusions, but the sibling boundaries are obvious from names and descriptions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.